1 Técnicas

1.1 Según tipo de aprendizaje:

1.1.1 Aprendizaje Supervisado:

1.1.1.1 Desde estadística:

set.seed(123)
df <- tibble(x = c(rchisq(20, 2),rchisq(20, 10)), y = rep(c(0,1), each=20))

ggplot(df, aes(x, y))+
  geom_point()+
  geom_smooth(method = "glm", 
    method.args = list(family = "binomial"), 
    se = FALSE)

1.1.2 Aprendizaje No supervisado:

1.1.2.2 Reducción de dimensionalidad:

1.2 Según el tipo de datos:

1.2.1 Datos estructurados:

1.2.1.1 Datos tabulados

iris

1.2.1.2 Series de Tiempo:

1.2.1.3 Grafos:

1.2.1.4 Datos geográficos

1.2.2 Datos no estructurados:

1.2.2.1 texto:

1.2.2.2 imagenes

1.2.2.3 sonido

1.3 Desde el punto de vista del remuestreo:

  • Test-train
  • Cross-Validation

1.4 Desde el punto de vista de la optimización:

2 Otros temas

  • Trade-off Precision-recall

---
title: Mapa de Data Science
output:
  html_notebook:
    number_sections: true
    toc: yes
    toc_float: yes
date: "`r format(Sys.time(), '%d %B %Y')`"
---



```{r message=FALSE, warning=FALSE, include=FALSE}
library(tidyverse)
library(datasets)
```

![](img/venn_ds.png){width=750}


![](img/map.svg){width=1000}

# Técnicas

## Según tipo de aprendizaje:

### Aprendizaje Supervisado:
#### Desde estadística:

- [Modelo Lineal](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)
    

```{r echo=FALSE}
ggplot(attitude, aes(rating, complaints))+
  geom_point()+
  geom_smooth(method = 'lm')
```
    
- [Ridge, Lasso, Elastic Net, Splines, etc.](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)

![](img/ridge_lasso.png){width=500}
  
- [Regresión logísitca](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)
  
```{r}
set.seed(123)
df <- tibble(x = c(rchisq(20, 2),rchisq(20, 10)), y = rep(c(0,1), each=20))

ggplot(df, aes(x, y))+
  geom_point()+
  geom_smooth(method = "glm", 
    method.args = list(family = "binomial"), 
    se = FALSE)
```

- [Support Vector Machine](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)

![](img/SVM_margin.png){width=500}

- [Naive Bayes](http://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf)
  
![](img/NB_1.png){width=500}
![](img/NB_2.png){width=750}
  
- [Markov chains](https://web.stanford.edu/~hastie/Papers/ESLII.pdf){width=500}
- ...


#### Desde computación:

- [Sistema experto](http://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf)
- [K nearest neighbors](http://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf)
![](img/knn.png)
- [Árboles de decisión](http://profsite.um.ac.ir/~monsefi/machine-learning/pdf/Machine-Learning-Tom-Mitchell.pdf)

![](img/what-is-a-decision-tree.png){width=750}    

- [Ensambles de árboles](https://doc.lagout.org/Others/Data%20Mining/Ensemble%20Methods%20in%20Data%20Mining_%20Improving%20Accuracy%20through%20Combining%20Predictions%20%5BSeni%20%26%20Elder%202010-02-24%5D.pdf)
- [Random Forest (Bagging)](https://bookdown.org/content/2031/ensambladores-random-forest-parte-i.html#random-forest)
![](img/RF.png){width=750}    
- [XG-BOOST (Boosting)](https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/)
    
![](img/boosted-trees-process.png){width=750}    

- Y muchos más...

#### Deep Learning:

- [Multi-layer perceptron network](https://www.deeplearningbook.org/contents/mlp.html)
![MLP](img/nn.png){width=500}
- [Convolutional Neural Network](http://cs231n.github.io/convolutional-networks/)
![CNN](img/cnn.png){width=1000}
- [Long-short Term Memory Neural Network](https://www.deeplearningbook.org/contents/rnn.html)
- ...
    

### Aprendizaje No supervisado:

#### Clustering:

- [K-means](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)
- [Patition around medioids](https://www.cs.umb.edu/cs738/pam1.pdf)
- [DBSCAN](https://elvex.ugr.es/idbis/dm/slides/43%20Clustering%20-%20Density.pdf)

![](img/cluster_comparison.png){width=500}
    
#### Reducción de dimensionalidad:
- [PCA](http://www.ub.edu/stat/personal/cuadras/metodos.pdf)
![](img/PCAexample.png){width=500}
- [LSI](http://lsa.colorado.edu/papers/dp1.LSAintro.pdf)
- [T-SNE](https://lvdmaaten.github.io/tsne/)
![](img/tsne.png){width=500}


### [Aprendizaje Semi-supervisado](https://www.geeksforgeeks.org/ml-semi-supervised-learning/)

![](img/semisupervised.png){width=750}

    

### [Modelos generativos](https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/ch01.html)

Por ejemplo: [https://transformer.huggingface.co/doc/gpt2-large](https://transformer.huggingface.co/doc/gpt2-large)

## Según el tipo de datos:

### Datos estructurados:

#### Datos tabulados

```{r}
iris
```

#### Series de Tiempo:

![](img/timeseries.png){width=750}

- [ARIMA](https://otexts.com/fpp2/arima.html)
- [Wavelets](http://sedici.unlp.edu.ar/bitstream/handle/10915/24289/Documento_completo.pdf?sequence=1)

![](img/wavelets.png){width=500}

#### Grafos:

![](img/network.png){width=500}

#### Datos geográficos

![](img/geodata.gif){width=500}

### Datos no estructurados:
#### texto:

- [LDA](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)

![](img/LDA.png){width=500}

- [Word Embeddings](https://medium.com/@gruizdevilla/introducci%C3%B3n-a-word2vec-skip-gram-model-4800f72c871f)

![](img/word_embeddings.png){width=500}

#### imagenes
#### sonido


## Desde el punto de vista del remuestreo:

- Test-train
- Cross-Validation

![](img/testtrainsplit.png){width=500}


- [Bootstraping](https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method/)

![](img/bootstrap-resampling.png){width=750}


## Desde el punto de vista de la optimización:
- [Gradient Descent](https://hackernoon.com/gradient-descent-aynk-7cbe95a778da)

![](img/gradientdescent.png){width=500}


- [Algorítmos Genéticos](https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3)

![](img/gadiagram.png){width=500}

- [Inferencia Variacional](https://arxiv.org/pdf/1601.00670.pdf)


# Otros temas

- [Sobreajuste](https://diegokoz.shinyapps.io/overfitting/)

![](img/overfitting.png){width=750}


- [Trade-off sesgo varianza](http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf)

![](img/biasvariance.jpg){width=500}

- Trade-off Precision-recall

![](img/precrecall.png){width=500}

# Implementaciones:

- [Caret](http://topepo.github.io/caret/index.html)
- [Tidymodels](https://rviews.rstudio.com/2019/06/19/a-gentle-intro-to-tidymodels/)
- [Sci-kit learn](https://scikit-learn.org/stable/)

- [H20](https://www.h2o.ai/)

- [Google Cloud](https://cloud.google.com/ml-engine/?hl=es-419)

- [Watson IBM](https://www.ibm.com/watson)






